Progressive Failure Analysis of Composite/Aluminum Riveted Joints Subjected to Pull-Through Loading

Out-of-plane mechanical properties of the riveted joints restrict the performance of the wing box assembly of airplane.It is necessary to investigate the pull-through performance of the composite/metal riveted joints in order to guide the riveting design and ensure the safety of the wing box assembly.The progressive failure mechanism of composite/aluminum riveted joint subjected to pull-through loading was investigated by experiments and finite element method.A progressive damage model based on the Hashin-type criteria and zero-thickness cohesive zone method was developed by VUMAT subroutine,which was validated by both open-hole tensile test and three-point bending test.Predicted load-displacement response,failure modes and damage propagation were analysed and compared with the results of the pull-through tests.There are 4 obvious characteristic stages on the load-displacement curve of the pull-through test and that of the finite element model:first load take-up stage,damage stage,second load take-up stage and failure stage.Relative error of stiffness,first load peak and second load peak between finite element method and experiments were 8.1%,-3.3%and 10.6%,respectively.It was found that the specimen was mainly broken by rivet-penetration fracture and delamination of plies of the composite laminate.And the material within the scope of the rivet head is more dangerous with more serious tensile damages than other regions,especially for 90°plies.This study proposes a numerical method for damage prediction and reveals the progressive failure mechanism of the hybrid material riveted joints subjected to the pull-through loading.

Numerical investigation on the position of holes for reducing stress concentration in composite plates with bolted and riveted joints

This paper studies the effects of fiber orientaion and holes position on stress concentration and the determination of weakened areas in the composite of glass fiber reinforced epoxy resin around the hole for joints by using the finite element method.In this study,for the observation of areas affected by stress concentration Tsai-Wu failure criterion is used to determine the failed elements and ANSYS Software is implemented for modeling.In order to compare the effect of geometric parameters on stress concentration around the holes,two types of hole position arrangement along with fibers orientation have been studied.Results show that the stress concentration coefficient is lower in the second type of holes arrangement in comparison with the first type for the same component dimensions.Increasing the distance from hole center to upper or lower edge of the sample and also decreasing the distance between holes,would result in an increase in the stress concentration.

A data-driven approach for predicting the fatigue life and failure mode of self-piercing rivet joints

In lightweight automotive vehicles,the application of self-piercing rivet(SPR)joints is becoming increasingly widespread.Considering the importance of automotive service performance,the fatigue performance of SPR joints has received considerable attention.Therefore,this study proposes a data-driven approach to predict the fatigue life and failure modes of SPR joints.The dataset comprises three specimen types:cross-tensile,cross-peel,and tensile-shear.To ensure data consistency,a finite element analysis was employed to convert the external loads of the different specimens.Feature selection was implemented using various machine-learning algorithms to determine the model input.The Gaussian process regression algorithm was used to predict fatigue life,and its performance was compared with different kernel functions commonly used in the field.The results revealed that the Matern kernel exhibited an exceptional predictive capability for fatigue life.Among the data points,95.9%fell within the 3-fold error band,and the remaining 4.1%exceeded the 3-fold error band owing to inherent dispersion in the fatigue data.To predict the failure location,various tree and artificial neural network(ANN)models were compared.The findings indicated that the ANN models slightly outperformed the tree models.The ANN model accurately predicts the failure of joints with varying dimensions and materials.However,minor deviations were observed for the joints with the same sheet.Overall,this data-driven approach provided a reliable predictive model for estimating the fatigue life and failure location of SPR joints.